In the last decade, automotive companies have significantly invested in innovation concerning many aspects of Automatic Driver Assistance Systems (ADAS). Due to the increasing attention toward automotive smart systems, much effort has been expended in terms of new hardware and software equipment.
For example, modern cars may use back, forward and/or side cameras for different purposes. Some of the most popular applications are, for example: Cross Traffic Alert (CTA), Lane Departure Warning (LDW), Collision Avoidance (CA) and Blind Spot Detection (BSD).
The different Advanced Driver Assistance Systems (ADAS) solutions may be used advantageously in different road scenarios. For example, the Cross Traffic Alert (CTA) may be useful in city road environments where other vehicles can cross the road. Conversely, Lane Departure Warning (LDW) or Blind Spot Detection (BSD) may be useful on highways where the car reaches high speeds and a brief distraction of the driver can lead to an accident.
Therefore, a wide range of advanced technologies are currently being introduced into production automobiles, with investments being made in terms of innovation about many aspects regarding Advanced Driver Assistance Systems (ADAS).
As said before, an Advanced Driver Assistance System (ADAS) is a vehicle control system that uses environment sensors (for example, radar, laser, vision, image camera) and the processing of environment information to improve traffic safety by assisting the driver in recognizing and reacting to potentially dangerous traffic situations.
Different types of intelligent vehicle assistance systems are used in driver information systems, for example:                advanced route navigation systems, as described in: S. Jeong, T. Kim, J. Lee, S. Lee and J. Lee, “Design analysis of precision Navigation System”, 12th International Conference on Control, Automation and Systems (ICCAS), 2012 (incorporated by reference);        driver warning systems, like Lane Departure Warning (LWD) systems, as described in: T. Aung and M. H. Zaw, “Video Based Lane Departure Warning System”, International Conference on Advances in Engineering and Technology, 2014 (incorporated by reference);        Collision Avoidance (CA), an example of which is disclosed in: A. Choudhary, Y. Moon and R. Nawkhare, “Design and Implementation of Wireless Security System in Vehicle”, International Journal of Innovative Research in Computer and Communication Engineering, 2015 (incorporated by reference);        Blind Spot Detection (BSD), like the solution described in: B. F. Wu, C. C. Kao, Y. F. Li and M. Y. Tsai, “A Real-Time Embedded Blind Spot Safety Assistance System”, International Journal of Vehicular Technology, 2012 (incorporated by reference); and        intervening systems, like Adaptive Cruise Control (ACC), an example of which is described in: P. Shakouri, J. Czeczot and A. Ordys, “Adaptive Cruise Control System using Balance-Based Adaptive Control technique”, 17th International Conference on Methods and Models in Automation and Robotics (MMAR), 2012 (incorporated by reference).        
In particular, the driver warning systems actively warn the driver of a potential danger, allowing the driver to take appropriate corrective actions in order to mitigate or completely avoid the dangerous event.
Among these systems, in addition to the security aid, Cross Traffic Alert (CTA) is an important system to reduce the stress felt by the driver, as disclosed in the document: B. Reimer, B. Mehler and J. F. Coughlin, “An Evaluation of Driver Reactions to New Vehicle Parking Assist Technologies Developed to Reduce Driver Stress”, New England University Transportation Center, White Paper, 2010 (incorporated by reference).
All of these systems are designed to alert drivers, for example with acoustic warning signal sounds, of the presence of encroaching vehicles. This warning can be useful in different situations, like backing out of parking spaces, and/or slowly arriving/leaving to traffic lights or crossroads.
A physical limitation of the Cross Traffic Alert (CTA) system is that the sensors cannot reveal obstructing objects or vehicles in the scene, so in this case cannot properly work.
The Cross Traffic Alert (CTA) system requires efficient algorithms and methods for real-time processing of the information collected. A range sensor mounted on the vehicle could provide a practical solution to the problem.
Typically a radar sensor, or both radar and image sensors, have been proposed for this purpose, as described for example in United States Patent Application Publication Nos. 2014/0015693 and 2011/0133917 (both incorporated by reference).
These known systems achieve good performance, but they are too expensive to enter the automotive mass market.
Moreover, interesting approaches to the problem are the data fusion techniques, which combine information from several sensors in order to provide a complete view of the environment.
Furthermore, different well performing approaches have also been proposed, like image infrared and visible light sensors, as described for example in: J. Thomanek and G. Wanielik, “A new pixel-based fusion framework to enhance object detection in automotive applications”, 17th International Conference on Information Fusion (FUSION), 2014 (incorporated by reference), and object detection sensor (radar or camera), and in-vehicle sensor (for example for steering wheel and speedometer) as disclosed in United States Patent Application Publication No. 2008/0306666 (incorporated by reference).
Unfortunately, these less expensive systems are still too costly to be suitable for a potential automotive mass market.
Since the need is felt for very low-cost systems, the attention has been focused only to the use of a single low cost image camera.
In the art, different approaches, based on a single low cost image camera, have been proposed:                histogram back-projection based road-plane segmentation, based on saturation and value channels of the video as described in: R. Tan, “A safety concept for camera based ADAS based on multicore MCU”, IEEE International Conference on Vehicular Electronics and Safety (ICVES), 2014 (incorporated by reference);        video based size and position of the vehicle, as described in: E. Dagan, O. Mano, G. P. Stein and A. Shashua, “Forward collision warning with a single camera”, IEEE Intelligent Vehicles Symposium, 2004 (incorporated by reference);        vehicle detection based on Haar and Adaboost and camera calibration, as described in J. Cui, F. Liu, Z. Li and Z. Jia, “Vehicle Localisation Using a Single Camera”, IEEE Intelligent Vehicles Symposium, 2010 (incorporated by reference);        Bayes classifier and shadow detection with symmetry-based approach, as described in: Y. Jheng, Y. Yen and T. Sun, “A symmetry-based forward vehicle detection and collision warning system on Android smartphone”, IEEE International Conference on Consumer Electronics—Taiwan (ICCE-TW), 2015 (incorporated by reference);        Haar-like feature and Adaboost classifier, together with Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature as disclosed in: Y. Deng, H. Liang, Z. Wang and J. Huang, “An integrated forward collision warning system based on monocular vision”, IEEE International Conference on Robotics and Biomimetics (ROBIO), 2014 (incorporated by reference);        SVM classifier as disclosed in: E. Salari and D. Ouyang, “Camera-based Forward Collision and lane departure warning systems using SVM”, IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS), 2013 (incorporated by reference), and so on.        
At the system level, these approaches are typically based on a combination of an image sensor and an image processor, as described for example in: United States Patent Application Publication No. 2010/0201508 (incorporated by reference), and usually on Engine Control Unit (ECU) with multi-core (Micro Controller Unit) MCU as described by: V. Balisavira and V. K. Pandey, “Real-time Object Detection by Road Plane Segmentation Technique for ADAS”, Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS), 2012 (incorporated by reference) to intensively elaborate the image data.
The main drawbacks of the cited prior art techniques are the need of at least an external Image Processor for heavy computation on the image data and the need of in-vehicle sensor (gear status, vehicle speed) to refine the results.
There is accordingly a need in the art to provide solutions for low cost Cross Traffic Alert method and system.